PSC article analysis Q1 - CZECH

1. Hypothesis: PSC is associated with the gut microbiome changes.

Pre_LTx vs Post_LTx vs Healthy analysis on merged data

• Alpha diversity –> group effect, country effect, interaction effect

• Beta diversity – PERMANOVA, PCA -> group effect, country effect, interaction effect

• DAA ->

  • o Group effect – linDA + MaAsLin2 intersection

  • o Country effect – linDA + MaAsLin2 union

Pre_ltx vs Healthy – by this comparison, we will find the effect of disease, liver cirrhosis and overall bad clinical condition

Pre_ltx vs post_ltx – by this comparison, we will find the effect of transplantation + general improvement of the clinical state

Post_ltx vs Healthy – by this comparison, we will find if the transplantation leads to „healthy microbiome”

TO DO —- Hopefully, we will find that all of these groups are somehow significantly different.

Regarding Post_LTx vs Healthy analysis, we can assume, that the differentially abundant taxa between these two groups remain because of the persistent gut MB alteration due to the PSC disease. Also, transplantation itself could add to this difference, as it has definitely strong impact on microbial composition.

Regarding Pre_LTx vs Healthy analysis, we can assume, that the differentially abundant taxa between these two groups are caused by PSC disease and other factors, like liver cirrhosis and overall bad clinical condition. To get only PSC associated taxa, we can intersect differentially abundant taxa from these aforementioned analyses (see diagram below).

imagename

TO DO: IMPORTOVAT TABULKU 1

source("custom_functions.R")

Data Import

Importing ASV, taxa and metadata tables for both Czech and Norway samples.

Czech

path = "../../data/analysis_ready_data/ikem/"
asv_tab_ikem <- as.data.frame(fread(file.path(path,"asv_table_ikem.csv"),
                                    check.names = FALSE))
taxa_tab_ikem <- as.data.frame(fread(file.path(path,"taxa_table_ikem.csv"),
                                     check.names = FALSE))
metadata_ikem <- as.data.frame(fread(file.path(path,"metadata_ikem.csv"),
                                     check.names = FALSE))

Spliting to segments

TO DO: STATISTICS OF READS

Merging two countries based on the different matrices - Ileum, Colon.

Terminal ileum

ileum_data <- merging_data(asv_tab_1=asv_tab_ikem,
                           asv_tab_2=NULL,
                           taxa_tab_1=taxa_tab_ikem,
                           taxa_tab_2=NULL,
                           metadata_1=metadata_ikem,
                           metadata_2=NULL,
                           segment="TI",Q="Q1")

## Removing 1498 ASV(s)

ileum_asv_tab <- ileum_data[[1]]
ileum_taxa_tab <- ileum_data[[2]]
ileum_metadata <- ileum_data[[3]]

Colon

colon_data <- merging_data(asv_tab_1=asv_tab_ikem,
                           asv_tab_2=NULL,
                           taxa_tab_1=taxa_tab_ikem,
                           taxa_tab_2=NULL,
                           metadata_1=metadata_ikem,
                           metadata_2=NULL,
                           segment="colon",Q="Q1")

## Removing 739 ASV(s)

colon_asv_tab <- colon_data[[1]]
colon_taxa_tab <- colon_data[[2]]
colon_metadata <- colon_data[[3]]

Data Analysis - Terminal ileum

segment="terminal_ileum"

Filtering

Rules: - prevalence > 5% (per group) - nearZeroVar with default settings - sequencing depth > 5000 - taxonomic assignment at least order

Library size

read_counts(ileum_asv_tab, line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Sequencing depth

data_filt <- seq_depth_filtering(ileum_asv_tab,
                                 ileum_taxa_tab,
                                 ileum_metadata,
                                 seq_depth_threshold = 10000)

## Removing 73 ASV(s)

filt_ileum_asv_tab <- data_filt[[1]]; alpha_ileum_asv_tab <- filt_ileum_asv_tab
filt_ileum_taxa_tab <- data_filt[[2]]; alpha_ileum_taxa_tab <- filt_ileum_taxa_tab
filt_ileum_metadata <- data_filt[[3]]; alpha_ileum_metadata <- filt_ileum_metadata

seq_step <- dim(filt_ileum_asv_tab)[1]

Library size

read_counts(filt_ileum_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

NearZeroVar

data_filt <- nearzerovar_filtering(filt_ileum_asv_tab, 
                                   filt_ileum_taxa_tab,
                                   filt_ileum_metadata)

filt_ileum_asv_tab <- data_filt[[1]]
filt_ileum_taxa_tab <- data_filt[[2]]
nearzero_step <- dim(filt_ileum_asv_tab)[1]

Library size

read_counts(filt_ileum_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Final Counts

final_counts_filtering(ileum_asv_tab,
                       filt_ileum_asv_tab,
                       filt_ileum_metadata,
                       seq_step, 0, nearzero_step) %>% `colnames<-`("Count")

Alpha diversity

path = "../results/Q1_czech/alpha_diversity"

Calculation

# Construct MPSE object
alpha_ileum_metadata$Sample <- alpha_ileum_metadata$SampleID
ileum_mpse <- as.MPSE(construct_phyloseq(alpha_ileum_asv_tab,
                                         alpha_ileum_taxa_tab,
                                         alpha_ileum_metadata))

ileum_mpse %<>% mp_rrarefy(raresize = 10000,seed = 123)

# Calculate alpha diversity - rarefied counts
ileum_mpse %<>% mp_cal_alpha(.abundance=RareAbundance, force=TRUE)

alpha_div_plots <- list()

# preparing data frame
alpha_data <- data.frame(SampleID=ileum_mpse$Sample.x,
                         Observe=ileum_mpse$Observe,
                         Shannon=ileum_mpse$Shannon,
                         Simpson=ileum_mpse$Simpson,
                         Pielou=ileum_mpse$Pielou,
                         Group=ileum_mpse$Group,
                         Country=ileum_mpse$Country)

write.csv(alpha_data,file.path(path,paste0("alpha_indices_",segment,".csv")),
          row.names = FALSE)

Plots

Custom plot

alpha_data <- alpha_data %>% 
  dplyr::select(-c("Simpson","Pielou")) %>%
  mutate(Richness=Observe)

p_A <- alpha_diversity_custom_2(alpha_data,
                                size = 1.5,
                                width = 0.3)

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

# save the results
alpha_div_plots[[paste(segment,"Custom")]] <- p_A

p_A

Linear Model

path = "../results/Q1_czech/alpha_diversity"
alpha_data <- read.csv(file.path(path,paste0("alpha_indices_",segment,".csv")))

Richness

results_model <- pairwise.lm(formula = "Observe ~ Group",
                             factors=alpha_data$Group,
                             data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_observe <- results_model[[1]]
  results_model_observe_emeans <- results_model[[2]]
} else {
  results_model_observe <- results_model
  results_model_observe_emeans <- NA
}

# save the results
pc_observed <- list(); 
pc_observed[[segment]] <- results_model_observe

# see the results
knitr::kable(results_model_observe,digits = 3,
caption = "Raw results of linear model of richness estimation.")

Raw results of linear model of richness estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-53.267	12.976	-4.105	0.000	0.000	***
pre_ltx vs Grouppost_ltx	25.120	11.755	2.137	0.035	0.035	*
healthy vs Grouppost_ltx	-28.147	9.269	-3.037	0.003	0.004	**

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	t value	Pr(>|t|)
healthy	167.892	7.940	21.145	0
pre_ltx	114.625	10.576	10.838	0
healthy	167.892	8.139	20.628	0

Shannon

results_model <- pairwise.lm(formula = "Shannon ~ Group",
                             factors=alpha_data$Group,
                             data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_shannon <- results_model[[1]]
  results_model_shannon_emeans <- results_model[[2]]
} else {
  results_model_shannon <- results_model
  results_model_shannon_emeans <- NA
}

# save the results
pc_shannon <- list(); 
pc_shannon[[segment]] <- as.data.frame(results_model_shannon)

# see the results
knitr::kable(results_model_shannon,digits = 3,
caption = "Raw results of linear model of Shannon estimation.")

Raw results of linear model of Shannon estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.506	0.175	-2.894	0.005	0.016	*
pre_ltx vs Grouppost_ltx	0.372	0.147	2.521	0.013	0.019	*
healthy vs Grouppost_ltx	-0.134	0.110	-1.217	0.226	0.226

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	t value	Pr(>|t|)
healthy	3.600	0.094	38.165	0
pre_ltx	3.094	0.133	23.320	0
healthy	3.600	0.110	32.844	0

Simpson

results_model <- pairwise.lm(formula = "Simpson ~ Group",
                                     factors=alpha_data$Group,
                                     data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_simpson <- results_model[[1]]
  results_model_simpson_emeans <- results_model[[2]]
} else {
  results_model_simpson <- results_model
  results_model_simpson_emeans <- NA
}


# save the results
pc_simpson <- list(); 
pc_simpson[[segment]] <- as.data.frame(results_model_simpson)

# see the results
knitr::kable(results_model_simpson,digits = 3,
caption = "Raw results of linear model of Simpson estimation.")

Raw results of linear model of Simpson estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.047	0.020	-2.339	0.023	0.068
pre_ltx vs Grouppost_ltx	0.037	0.021	1.776	0.078	0.117
healthy vs Grouppost_ltx	-0.009	0.016	-0.577	0.565	0.565

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	t value	Pr(>|t|)
healthy	0.934	0.014	68.163	0
pre_ltx	0.887	0.019	46.712	0
healthy	0.934	0.013	74.536	0

Pielou

results_model <- pairwise.lm(formula = "Pielou ~ Group",
                                     factors=alpha_data$Group,
                                     data=alpha_data)

# check interaction

if (!is.data.frame(results_model)){
  results_model_pielou <- results_model[[1]]
  results_model_pielou_emeans <- results_model[[2]]
} else {
  results_model_pielou <- results_model
  results_model_pielou_emeans <- NA
}

# save the results
pc_pielou <- list(); 
pc_pielou[[segment]] <- as.data.frame(results_model_pielou)

# see the results
knitr::kable(results_model_pielou,digits = 3,
caption = "Raw results of linear model of Pielou estimation.")

Raw results of linear model of Pielou estimation.

	Estimate	Std..Error	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.042	0.025	-1.671	0.100	0.150
pre_ltx vs Grouppost_ltx	0.043	0.021	2.012	0.046	0.139
healthy vs Grouppost_ltx	0.001	0.017	0.079	0.937	0.937

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	t value	Pr(>|t|)
healthy	0.706	0.014	48.736	0
pre_ltx	0.664	0.019	34.587	0
healthy	0.706	0.016	45.174	0

Saving results

alpha_list <- list(
  Richness=pc_observed[[segment]] %>% rownames_to_column("Comparison"),
  Shannon=pc_shannon[[segment]] %>% rownames_to_column("Comparison"),
  Simpson=pc_simpson[[segment]] %>% rownames_to_column("Comparison"),
  Pielou=pc_pielou[[segment]] %>% rownames_to_column("Comparison"))
                   
write.xlsx(alpha_list, 
           file = file.path(path,paste0("alpha_diversity_results_",segment,".xlsx")))

Beta diversity

Calculating Aitchison distance (euclidean distance on clr-transformed data), both at ASV and genus level.

Main analysis - Genus, Aitchison

Genus level, Aitchison distance

level="genus"

path = "../results/Q1_czech/beta_diversity"

pairwise_aitchison_raw <- list()
pca_plots_list <- list()

Aggregation, filtering

# Aggregation
genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level=level,
                             names=TRUE)
# Filtration
filt_data <- filtering_steps(genus_data[[1]],
                             genus_data[[2]],
                             ileum_metadata,
                             seq_depth_threshold=10000)

## Removing 3 ASV(s)

filt_ileum_genus_tab <- filt_data[[1]]
filt_ileum_genus_taxa <- filt_data[[2]]
filt_ileum_metadata_genus <- filt_data[[3]]

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata_genus$Group,
                           covariate = NULL, 
                           sim.method = "robust.aitchison", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
pairwise_aitchison_raw[[paste(level, segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	285.032	1.705	0.028	0.002	0.003	**
pre_ltx vs post_ltx	1	192.724	1.059	0.008	0.305	0.305
post_ltx vs healthy	1	488.822	2.712	0.019	0.001	0.003	**

Plots

Custom

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata_genus,
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
pca_plots_list[[paste(segment,level,"custom")]] <- p

# see the results
p

Saving results

write.xlsx(pairwise_aitchison_raw[[paste(level, segment)]], 
           file = file.path(path,
           paste0("beta_diversity_results_", segment,".xlsx")))

Supplementary analysis

supplements_beta <- list()

Genus level

level="genus"

Bray-Curtis

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata_genus$Group,
                           covariate = NULL, 
                           sim.method = "bray", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.412	2.198	0.036	0.008	0.012	*
pre_ltx vs post_ltx	1	0.230	1.052	0.008	0.350	0.350
post_ltx vs healthy	1	0.771	3.752	0.027	0.001	0.003	**

Plots

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata_genus,
                                 measure = "bray",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

pairwise_df <- filt_ileum_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata_genus$Group,
                           covariate = NULL, 
                           sim.method = "jaccard", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.499	1.835	0.030	0.005	0.007	**
pre_ltx vs post_ltx	1	0.333	1.101	0.009	0.262	0.262
post_ltx vs healthy	1	0.865	2.980	0.021	0.001	0.003	**

Plots

Custom

p <- pca_plot_custom(filt_ileum_genus_tab,
                                 filt_ileum_genus_taxa,
                                 filt_ileum_metadata_genus,
                                 measure = "jaccard",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

ASV level

level="ASV"

Aitchison

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,
                           covariate = NULL, 
                           sim.method = "robust.aitchison", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("aitchison",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	484.192	1.552	0.026	0.002	0.003	**
pre_ltx vs post_ltx	1	320.395	1.130	0.009	0.121	0.121
post_ltx vs healthy	1	877.994	2.987	0.021	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                           filt_ileum_taxa_tab,
                           filt_ileum_metadata,
                           show_boxplots = TRUE,
                           variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA aitchison",level,segment)]] <- p

# see the results
p

Bray-Curtis

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,
                           covariate = NULL, 
                           sim.method = "bray", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.566	1.748	0.029	0.007	0.011	*
pre_ltx vs post_ltx	1	0.437	1.284	0.010	0.113	0.113
post_ltx vs healthy	1	1.212	3.689	0.026	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                     filt_ileum_taxa_tab,
                     filt_ileum_metadata,
                     measure = "bray",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

# preparing data frame
pairwise_df <- filt_ileum_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_ileum_metadata$Group,
                           covariate = NULL, 
                           sim.method = "jaccard", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.558	1.423	0.024	0.008	0.012	*
pre_ltx vs post_ltx	1	0.474	1.174	0.009	0.102	0.102
post_ltx vs healthy	1	1.027	2.597	0.019	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_ileum_asv_tab,
                     filt_ileum_taxa_tab,
                     filt_ileum_metadata,
                     measure = "jaccard",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

Saving results

write.xlsx(supplements_beta[!grepl("PCoA",names(supplements_beta))],
           file = file.path(path,
           paste0("supplements_beta_diversity_", segment,".xlsx")))

Univariate Analysis

Main - Genus level

level="genus"

# needed paths
path = "../results/Q1_czech/univariate_analysis"
path_maaslin=file.path("../intermediate_files/maaslin/Q1_czech",level)

# variables
raw_linda_results_genus <- list();
raw_linda_results_genus[[segment]] <- list()
linda_results_genus <- list(); 
linda_results_genus[[segment]] <- list()

# country and interaction problems
list_country_union <- list()
list_intersections <- list()
list_venns <- list()
uni_statistics <- list()

# workbook for final df
wb <- createWorkbook()

# PSC effect
psc_effect <- list()

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

ileum_genus_asv_taxa_tab <- create_asv_taxa_table(ileum_genus_tab,
                                                  ileum_genus_taxa_tab)

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  61  samples and  212  features will be tested!

## Warning in linda(filt_ileum_uni_data, filt_ileum_uni_metadata, formula = "~ Group"): Some features have less than 3 nonzero values! 
##                      They have virtually no statistical power. You may consider filtering them in the analysis!

## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano_1)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)

## Warning: Removed 10 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  126  samples and  196  features will be tested!

## Warning in linda(filt_ileum_uni_data, filt_ileum_uni_metadata, formula = "~ Group"): Some features have less than 3 nonzero values! 
##                      They have virtually no statistical power. You may consider filtering them in the analysis!

## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)


volcano <- ggarrange(volcano_1)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)

## Warning: Removed 1 row containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_genus_tab,
                            ileum_genus_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  139  samples and  194  features will be tested!
## Pseudo-count approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano_1)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)

## Warning: Removed 22 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -1.585133 
## max_clr 7.006078 
## min_log -5.092591 
## max_log 5.066079

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(wb,file.path(path,paste0("uni_analysis_wb_",segment,".xlsx")),
             overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect[[paste(segment,level)]],file.path(path,paste0("psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa

write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("significant_taxa_",segment,".xlsx")))

Supplementary Analysis

supplements_uni <- list()
supplements_wb <- createWorkbook()

ASV level

level="ASV"

path_maaslin="../intermediate_files/maaslin/Q1_czech/ASV/"

raw_linda_results <- list();
raw_linda_results[[segment]] <- list()
linda_results <- list(); 
linda_results[[segment]] <- list()

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  61  samples and  738  features will be tested!

## Warning in linda(filt_ileum_uni_data, filt_ileum_uni_metadata, formula = "~ Group"): Some features have less than 3 nonzero values! 
##                      They have virtually no statistical power. You may consider filtering them in the analysis!

## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano_1, ncol=3)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1, ncol=2)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  126  samples and  526  features will be tested!

## Warning in linda(filt_ileum_uni_data, filt_ileum_uni_metadata, formula = "~ Group"): Some features have less than 3 nonzero values! 
##                      They have virtually no statistical power. You may consider filtering them in the analysis!

## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano_1)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_asv_tab,
                            ileum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  139  samples and  617  features will be tested!

## Warning in linda(filt_ileum_uni_data, filt_ileum_uni_metadata, formula = "~ Group"): Some features have less than 3 nonzero values! 
##                      They have virtually no statistical power. You may consider filtering them in the analysis!

## Pseudo-count approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)


volcano <- ggarrange(volcano_1)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)


volcano <- ggarrange(volcano1)
volcano

## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -0.7209313 
## max_clr 6.386569 
## min_log -5.127704 
## max_log 4.154206

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Phylum level

level="phylum"

path_maaslin="../intermediate_files/maaslin/Q1_czech/Phylum/"

raw_linda_results_phylum <- list();
raw_linda_results_phylum[[segment]] <- list()
linda_results_phylum <- list(); 
linda_results_phylum[[segment]] <- list()

Aggregate taxa

phylum_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = "Phylum")

ileum_phylum_tab <- phylum_data[[1]]
ileum_phylum_taxa_tab <- phylum_data[[2]]

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  61  samples and  9  features will be tested!
## Pseudo-count approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano <- ggarrange(volcano_1)

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

## Removing 2 ASV(s)

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  126  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano <- ggarrange(volcano_1)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(ileum_phylum_tab,
                            ileum_phylum_taxa_tab,
                            ileum_metadata,
                            group, usage="linDA")

filt_ileum_uni_data <- linda_data[[1]]
filt_ileum_uni_taxa <- linda_data[[2]]
filt_ileum_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_ileum_uni_data, 
                   filt_ileum_uni_metadata, 
                   formula = '~ Group')

## 0  features are filtered!
## The filtered data has  139  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results_phylum[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_ileum_uni_data,
                filt_ileum_uni_taxa)
  
  linda_results_phylum[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_ileum_uni_data,
             filt_ileum_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, 
                                group1, 
                                taxa_table = filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

## Using Phylum for naming

volcano <- ggarrange(volcano_1)
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_ileum_uni_taxa) + 
            ggtitle(comparison_name)

volcano <- ggarrange(volcano1)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_phylum, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_phylum[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      ileum_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -4.023876 
## max_clr -0.6380426 
## min_log -2.665094 
## max_log 2.803749

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(supplements_wb,file.path(path,paste0("supplements_uni_analysis_wb_",segment,".xlsx")),overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect,
          file.path(path,paste0("supplements_psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa
write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("supplements_significant_taxa_",segment,".xlsx")))

Machine learning

path = "../results/Q1_czech/models"

ElasticNet

model="enet"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ <- list()
models_cm <- list()
betas <- list()
roc_cs <- list()

models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          4.4297736
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                                NaN
## auc_optimism_corrected          0.9125173
## auc_optimism_corrected_CIL      0.7460227
## auc_optimism_corrected_CIU      0.9719818
## accuracy                        1.0000000
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.8436514
## accuracy_optimism_corrected_CIL 0.6576087
## accuracy_optimism_corrected_CIU 0.9047619

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $czech
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                            0.0000000
## lambda                          23.6156255
## auc                              0.9485294
## auc_czech                        0.9485294
## auc_no                                 NaN
## auc_optimism_corrected           0.6948681
## auc_optimism_corrected_CIL       0.5824476
## auc_optimism_corrected_CIU       0.8110143
## accuracy                         0.8095238
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected      0.7919350
## accuracy_optimism_corrected_CIL  0.7250614
## accuracy_optimism_corrected_CIU  0.8608075

enet_model$conf_matrices

## $original
##     0  
## 0 102 0
## 1  24 0
## 
## $czech
##     0  
## 0 102 0
## 1  24 0
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.40000000
## lambda                          0.03937419
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.94206423
## auc_optimism_corrected_CIL      0.90105239
## auc_optimism_corrected_CIU      0.98566042
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.84400802
## accuracy_optimism_corrected_CIL 0.76024411
## accuracy_optimism_corrected_CIU 0.91915820

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $czech
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.1579673
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                                NaN
## auc_optimism_corrected          0.9130841
## auc_optimism_corrected_CIL      0.8491286
## auc_optimism_corrected_CIU      0.9958333
## accuracy                        1.0000000
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.8302828
## accuracy_optimism_corrected_CIL 0.6741848
## accuracy_optimism_corrected_CIU 0.9081169

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $czech
##     Predicted
## True  0  1
##    0 37  0
##    1  0 24
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          4.8657540
## auc                             0.9485294
## auc_czech                       0.9485294
## auc_no                                NaN
## auc_optimism_corrected          0.7094762
## auc_optimism_corrected_CIL      0.6273274
## auc_optimism_corrected_CIU      0.8340541
## accuracy                        0.8095238
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.7751889
## accuracy_optimism_corrected_CIL 0.6955785
## accuracy_optimism_corrected_CIU 0.8571172

enet_model$conf_matrices

## $original
##     0  
## 0 102 0
## 1  24 0
## 
## $czech
##     0  
## 0 102 0
## 1  24 0
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,group, 
                                     usage="ml_clr")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.40000000
## lambda                          0.02563917
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.95484421
## auc_optimism_corrected_CIL      0.92410714
## auc_optimism_corrected_CIU      0.99290392
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.88512801
## accuracy_optimism_corrected_CIL 0.83058511
## accuracy_optimism_corrected_CIU 0.93830733

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $czech
##     Predicted
## True   0   1
##    0  37   0
##    1   0 102
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Saving results

models_summ_df_ileum <- do.call(rbind, 
  models_summ[grep(segment,names(models_summ),value = TRUE)])

write.csv(models_summ_df_ileum,file.path(path,paste0("elastic_net_",segment,".csv")))

Supplementary models

supplements_models <- list()

CLR-transformed data

kNN

model="knn"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               19.0000000
## auc                              0.9481982
## auc_optimism_corrected           0.9111788
## auc_optimism_corrected_CIL       0.8459517
## auc_optimism_corrected_CIU       0.9668485
## accuracy                         0.8852459
## accuracy_optimism_corrected      0.8197064
## accuracy_optimism_corrected_CIL  0.7415761
## accuracy_optimism_corrected_CIU  0.9047619

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.8386438
## auc_optimism_corrected           0.6766450
## auc_optimism_corrected_CIL       0.5849351
## auc_optimism_corrected_CIU       0.7871080
## accuracy                         0.8333333
## accuracy_optimism_corrected      0.7752687
## accuracy_optimism_corrected_CIL  0.7326574
## accuracy_optimism_corrected_CIU  0.8345170

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               12.0000000
## auc                              0.9048755
## auc_optimism_corrected           0.8180820
## auc_optimism_corrected_CIL       0.7052114
## auc_optimism_corrected_CIU       0.9524700
## accuracy                         0.8848921
## accuracy_optimism_corrected      0.7978793
## accuracy_optimism_corrected_CIL  0.7233586
## accuracy_optimism_corrected_CIU  0.8995192

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9549550
## auc_optimism_corrected           0.8580076
## auc_optimism_corrected_CIL       0.7908357
## auc_optimism_corrected_CIU       0.9711779
## accuracy                         0.7540984
## accuracy_optimism_corrected      0.7098720
## accuracy_optimism_corrected_CIL  0.6110248
## accuracy_optimism_corrected_CIU  0.8055195

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               14.0000000
## auc                              0.8415033
## auc_optimism_corrected           0.6934488
## auc_optimism_corrected_CIL       0.5589627
## auc_optimism_corrected_CIU       0.8054960
## accuracy                         0.8174603
## accuracy_optimism_corrected      0.7932043
## accuracy_optimism_corrected_CIL  0.7117243
## accuracy_optimism_corrected_CIU  0.8937027

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               19.0000000
## auc                              0.9670111
## auc_optimism_corrected           0.8924815
## auc_optimism_corrected_CIL       0.8406156
## auc_optimism_corrected_CIU       0.9616798
## accuracy                         0.8848921
## accuracy_optimism_corrected      0.8002149
## accuracy_optimism_corrected_CIL  0.7323864
## accuracy_optimism_corrected_CIU  0.8894930

roc_c

Random Forest

model="rf"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "13"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8287759"
## auc_optimism_corrected_CIL      "0.5771336"
## auc_optimism_corrected_CIU      "0.9839286"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7672228"
## accuracy_optimism_corrected_CIL "0.6215217"
## accuracy_optimism_corrected_CIU "0.8724432"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "43"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.6561724"
## auc_optimism_corrected_CIL      "0.5778259"
## auc_optimism_corrected_CIU      "0.7348714"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7743705"
## accuracy_optimism_corrected_CIL "0.696875" 
## accuracy_optimism_corrected_CIU "0.8384067"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "149"      
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8650417"
## auc_optimism_corrected_CIL      "0.7467677"
## auc_optimism_corrected_CIU      "0.948142" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.846914" 
## accuracy_optimism_corrected_CIL "0.8"      
## accuracy_optimism_corrected_CIU "0.916964"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "121"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8817009"
## auc_optimism_corrected_CIL      "0.7688763"
## auc_optimism_corrected_CIU      "0.9631799"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.760551" 
## accuracy_optimism_corrected_CIL "0.6504891"
## accuracy_optimism_corrected_CIU "0.8621753"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "7"        
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.7082342"
## auc_optimism_corrected_CIL      "0.5029177"
## auc_optimism_corrected_CIU      "0.8629418"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7716928"
## accuracy_optimism_corrected_CIL "0.7083333"
## accuracy_optimism_corrected_CIU "0.8392045"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

Gradient boosting

model="gb"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_clr")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_clr")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "33"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8529435"
## auc_optimism_corrected_CIL      "0.7198997"
## auc_optimism_corrected_CIU      "0.9273955"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8159999"
## accuracy_optimism_corrected_CIL "0.7118762"
## accuracy_optimism_corrected_CIU "0.9344975"

roc_c

Relative abundances

Elastic net

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.2000000
## lambda                          0.3778764
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                                NaN
## auc_optimism_corrected          0.8454954
## auc_optimism_corrected_CIL      0.7061699
## auc_optimism_corrected_CIU      0.9437710
## accuracy                        1.0000000
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.7949445
## accuracy_optimism_corrected_CIL 0.6998447
## accuracy_optimism_corrected_CIU 0.8709821

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                             0.0000000
## lambda                          102.5526002
## auc                               0.9971405
## auc_czech                         0.9971405
## auc_no                                  NaN
## auc_optimism_corrected            0.5652716
## auc_optimism_corrected_CIL        0.3719551
## auc_optimism_corrected_CIU        0.8215233
## accuracy                          0.8095238
## accuracy_czech                          NaN
## accuracy_no                             NaN
## accuracy_optimism_corrected       0.7872688
## accuracy_optimism_corrected_CIL   0.7117021
## accuracy_optimism_corrected_CIU   0.8568182

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          3.5362260
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                                NaN
## auc_optimism_corrected          0.7951580
## auc_optimism_corrected_CIL      0.7080357
## auc_optimism_corrected_CIU      0.8688125
## accuracy                        0.8776978
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.7518252
## accuracy_optimism_corrected_CIL 0.6646915
## accuracy_optimism_corrected_CIU 0.8251270

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.0000000
## lambda                          8.2397352
## auc                             1.0000000
## auc_czech                       1.0000000
## auc_no                                NaN
## auc_optimism_corrected          0.8509168
## auc_optimism_corrected_CIL      0.7440741
## auc_optimism_corrected_CIU      0.9829545
## accuracy                        0.8032787
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.7805345
## accuracy_optimism_corrected_CIL 0.6731884
## accuracy_optimism_corrected_CIU 0.9339583

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                             0.0000000
## lambda                          130.6178958
## auc                               0.9791667
## auc_czech                         0.9791667
## auc_no                                  NaN
## auc_optimism_corrected            0.6084429
## auc_optimism_corrected_CIL        0.4915927
## auc_optimism_corrected_CIU        0.7402786
## accuracy                          0.8095238
## accuracy_czech                          NaN
## accuracy_no                             NaN
## accuracy_optimism_corrected       0.7930146
## accuracy_optimism_corrected_CIL   0.7350962
## accuracy_optimism_corrected_CIU   0.8823266

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                      [,1]
## alpha                           0.8000000
## lambda                          0.1166804
## auc                             0.9059353
## auc_czech                       0.9059353
## auc_no                                NaN
## auc_optimism_corrected          0.7806661
## auc_optimism_corrected_CIL      0.5468750
## auc_optimism_corrected_CIU      0.8714540
## accuracy                        0.7841727
## accuracy_czech                        NaN
## accuracy_no                           NaN
## accuracy_optimism_corrected     0.7021714
## accuracy_optimism_corrected_CIL 0.3905918
## accuracy_optimism_corrected_CIU 0.8397496

roc_c

kNN

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9110360
## auc_optimism_corrected           0.6990118
## auc_optimism_corrected_CIL       0.5201451
## auc_optimism_corrected_CIU       0.8744488
## accuracy                         0.6229508
## accuracy_optimism_corrected      0.5953868
## accuracy_optimism_corrected_CIL  0.4885093
## accuracy_optimism_corrected_CIU  0.7069805

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.8588644
## auc_optimism_corrected           0.6665429
## auc_optimism_corrected_CIL       0.5741466
## auc_optimism_corrected_CIU       0.8298764
## accuracy                         0.8095238
## accuracy_optimism_corrected      0.7956747
## accuracy_optimism_corrected_CIL  0.7117021
## accuracy_optimism_corrected_CIU  0.8823266

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               15.0000000
## auc                              0.8180975
## auc_optimism_corrected           0.7040794
## auc_optimism_corrected_CIL       0.6010464
## auc_optimism_corrected_CIU       0.7911414
## accuracy                         0.8129496
## accuracy_optimism_corrected      0.7108628
## accuracy_optimism_corrected_CIL  0.6023514
## accuracy_optimism_corrected_CIU  0.7983019

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.8023649
## auc_optimism_corrected           0.6724603
## auc_optimism_corrected_CIL       0.4564286
## auc_optimism_corrected_CIU       0.9615532
## accuracy                         0.6721311
## accuracy_optimism_corrected      0.6505327
## accuracy_optimism_corrected_CIL  0.5652174
## accuracy_optimism_corrected_CIU  0.7910173

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               30.0000000
## auc                              0.7640931
## auc_optimism_corrected           0.5967148
## auc_optimism_corrected_CIL       0.4683710
## auc_optimism_corrected_CIU       0.6899451
## accuracy                         0.8095238
## accuracy_optimism_corrected      0.7987181
## accuracy_optimism_corrected_CIL  0.7281915
## accuracy_optimism_corrected_CIU  0.8823266

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               16.0000000
## auc                              0.8449921
## auc_optimism_corrected           0.7102435
## auc_optimism_corrected_CIL       0.5709124
## auc_optimism_corrected_CIU       0.7964627
## accuracy                         0.7985612
## accuracy_optimism_corrected      0.7138748
## accuracy_optimism_corrected_CIL  0.6218794
## accuracy_optimism_corrected_CIU  0.8138019

roc_c

Random Forest

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "105"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9169639"
## auc_optimism_corrected_CIL      "0.7780303"
## auc_optimism_corrected_CIU      "0.9842147"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8188161"
## accuracy_optimism_corrected_CIL "0.6602717"
## accuracy_optimism_corrected_CIU "0.9047619"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "53"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.7942196"
## auc_optimism_corrected_CIL      "0.6562306"
## auc_optimism_corrected_CIU      "0.8874175"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7986889"
## accuracy_optimism_corrected_CIL "0.7250614"
## accuracy_optimism_corrected_CIU "0.8673445"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "71"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9228307"
## auc_optimism_corrected_CIL      "0.8516193"
## auc_optimism_corrected_CIU      "0.9663803"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8354697"
## accuracy_optimism_corrected_CIL "0.8078764"
## accuracy_optimism_corrected_CIU "0.8776488"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "93"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9215987"
## auc_optimism_corrected_CIL      "0.8507365"
## auc_optimism_corrected_CIU      "0.9755298"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8093709"
## accuracy_optimism_corrected_CIL "0.7078804"
## accuracy_optimism_corrected_CIU "0.8724432"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "29"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.7494602"
## auc_optimism_corrected_CIL      "0.6336292"
## auc_optimism_corrected_CIU      "0.8452175"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.7778113"
## accuracy_optimism_corrected_CIL "0.7247008"
## accuracy_optimism_corrected_CIU "0.8673445"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

Gradient boosting

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 1478 ASV(s)

## Removing 118 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 555 ASV(s)

## Removing 36 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_asv_tab,
                                     ileum_taxa_tab,
                                     ileum_metadata,
                                     group, usage="ml_ra")

## Removing 260 ASV(s)

## Removing 48 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(ileum_asv_tab,
                             ileum_taxa_tab,
                             taxonomic_level = level)

ileum_genus_tab <- genus_data[[1]]
ileum_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 93 ASV(s)

## Removing 8 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 41 ASV(s)

## Removing 3 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_ileum_uni_data <- binomial_prep(ileum_genus_tab,
                                     ileum_genus_taxa_tab,
                                     ileum_metadata,
                                     group, 
                                     usage="ml_ra")

## Removing 16 ASV(s)

## Removing 2 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_ileum_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "109"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.8691973"
## auc_optimism_corrected_CIL      "0.7834489"
## auc_optimism_corrected_CIU      "0.9416474"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8255819"
## accuracy_optimism_corrected_CIL "0.7641586"
## accuracy_optimism_corrected_CIU "0.9079108"

roc_c

Saving results

models_list <- list()

for (model_name in names(supplements_models$models_summ)){
  df <- do.call(rbind, supplements_models$models_summ[[model_name]])
  models_list[[model_name]] <- df
}

write.xlsx(models_list,
           file=file.path(path,paste0("supplements_models_",segment,".xlsx")),
           rowNames=TRUE)

Results overview

Alpha diversity

pc_observed[[segment]]

pc_shannon[[segment]]

pc_simpson[[segment]]

pc_pielou[[segment]]

alpha_div_plots[[paste(segment,"Custom")]]

Beta diversity

Main results

pairwise_aitchison_raw[[paste("genus", segment)]]

PCA

pca_plots_list[[paste(segment,"genus custom")]]

Supplements

knitr::kable(supplements_beta[!grepl("PCoA",names(supplements_beta))],
             digits = 3,
             caption = "Supplementary PERMANOVA results")

Supplementary PERMANOVA results

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.412 2.198 0.036 0.008 0.012 * pre_ltx vs post_ltx 1 0.230 1.052 0.008 0.350 0.350 post_ltx vs healthy 1 0.771 3.752 0.027 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.499 1.835 0.030 0.005 0.007 ** pre_ltx vs post_ltx 1 0.333 1.101 0.009 0.262 0.262 post_ltx vs healthy 1 0.865 2.980 0.021 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 484.192 1.552 0.026 0.002 0.003 ** pre_ltx vs post_ltx 1 320.395 1.130 0.009 0.121 0.121 post_ltx vs healthy 1 877.994 2.987 0.021 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.566 1.748 0.029 0.007 0.011 * pre_ltx vs post_ltx 1 0.437 1.284 0.010 0.113 0.113 post_ltx vs healthy 1 1.212 3.689 0.026 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.558 1.423 0.024 0.008 0.012 * pre_ltx vs post_ltx 1 0.474 1.174 0.009 0.102 0.102 post_ltx vs healthy 1 1.027 2.597 0.019 0.001 0.003 **

PCA

ggarrange(plotlist = supplements_beta[grepl("PCoA",names(supplements_beta))],
          labels=names(supplements_beta[grepl("PCoA",names(supplements_beta))]),
          font.label = list(size=5,face="plain"),
          ncol=2,nrow=3)

Univariate analysis

Number of significant taxa

knitr::kable(cbind(as.data.frame(lapply(list_intersections,nrow)),
      as.data.frame(lapply(psc_effect,nrow))) %>% t() %>% 
  `colnames<-`("Count") %>% 
  `rownames<-`(c(names(list_intersections),"PSC effect ASV","PSC effect Genus","PSC effect Phylum")),caption="Number of significant taxa")

Number of significant taxa

	Count
terminal_ileum genus healthy vs pre_ltx	23
terminal_ileum genus pre_ltx vs post_ltx	6
terminal_ileum genus healthy vs post_ltx	31
terminal_ileum ASV healthy vs pre_ltx	27
terminal_ileum ASV pre_ltx vs post_ltx	3
terminal_ileum ASV healthy vs post_ltx	76
terminal_ileum phylum healthy vs pre_ltx	1
terminal_ileum phylum pre_ltx vs post_ltx	0
terminal_ileum phylum healthy vs post_ltx	2
PSC effect ASV	12
PSC effect Genus	18
PSC effect Phylum	0

Machine learning

Main models

Summary

knitr::kable(models_summ_df_ileum %>% dplyr::select(
"alpha","lambda",
"auc_optimism_corrected",
"auc_optimism_corrected_CIL",
"auc_optimism_corrected_CIU"),
             digits=2,caption="Elastic net results")

Elastic net results

	alpha	lambda	auc_optimism_corrected	auc_optimism_corrected_CIL	auc_optimism_corrected_CIU
pre_ltx vs healthy ASV terminal_ileum	0.0	4.43	0.91	0.75	0.97
pre_ltx vs post_ltx ASV terminal_ileum	0.0	23.62	0.69	0.58	0.81
post_ltx vs healthy ASV terminal_ileum	0.4	0.04	0.94	0.90	0.99
pre_ltx vs healthy genus terminal_ileum	0.2	0.16	0.91	0.85	1.00
pre_ltx vs post_ltx genus terminal_ileum	0.0	4.87	0.71	0.63	0.83
post_ltx vs healthy genus terminal_ileum	0.4	0.03	0.95	0.92	0.99

ROC - ASV level

roc_curve_all_custom(roc_cs[c(1:3)], 
                     Q="Q1_czech",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy ASV terminal_ileum"  "pre_ltx vs post_ltx ASV terminal_ileum"
## [3] "post_ltx vs healthy ASV terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ROC - Genus level

roc_curve_all_custom(roc_cs[c(4:6)],Q="Q1_czech",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy genus terminal_ileum"  "pre_ltx vs post_ltx genus terminal_ileum"
## [3] "post_ltx vs healthy genus terminal_ileum"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

Supplementary models

Summary

# Build final dataframe
models_list[["enet_model"]] <- models_summ_df_ileum
final_df <- tibble(row_names = rownames(models_list[[1]]))

# Loop through models and extract required values
for (model_name in names(models_list)) {
  model_df <- models_list[[model_name]]
  
  # Combine AUC_optimism_corrected with its CI values
  final_df[[model_name]] <- paste0(
    round(model_df$auc_optimism_corrected, 3), 
    " (", round(model_df$auc_optimism_corrected_CIL, 3), "; ", 
    round(model_df$auc_optimism_corrected_CIU, 3), ")"
  )
}

knitr::kable(final_df, caption="All models")

All models

row_names	knn_model	rf_model	gbm_model	enet_model_ra	knn_model_ra	rf_model_ra	gbm_model_ra	enet_model
pre_ltx vs healthy ASV terminal_ileum	0.911 (0.846; 0.967)	0.829 (0.577; 0.984)	0.853 (0.72; 0.927)	0.845 (0.706; 0.944)	0.699 (0.52; 0.874)	0.917 (0.778; 0.984)	0.869 (0.783; 0.942)	0.913 (0.746; 0.972)
pre_ltx vs post_ltx ASV terminal_ileum	0.677 (0.585; 0.787)	0.656 (0.578; 0.735)	0.853 (0.72; 0.927)	0.565 (0.372; 0.822)	0.667 (0.574; 0.83)	0.794 (0.656; 0.887)	0.869 (0.783; 0.942)	0.695 (0.582; 0.811)
post_ltx vs healthy ASV terminal_ileum	0.818 (0.705; 0.952)	0.865 (0.747; 0.948)	0.853 (0.72; 0.927)	0.795 (0.708; 0.869)	0.704 (0.601; 0.791)	0.923 (0.852; 0.966)	0.869 (0.783; 0.942)	0.942 (0.901; 0.986)
pre_ltx vs healthy genus terminal_ileum	0.858 (0.791; 0.971)	0.882 (0.769; 0.963)	0.853 (0.72; 0.927)	0.851 (0.744; 0.983)	0.672 (0.456; 0.962)	0.922 (0.851; 0.976)	0.869 (0.783; 0.942)	0.913 (0.849; 0.996)
pre_ltx vs post_ltx genus terminal_ileum	0.693 (0.559; 0.805)	0.708 (0.503; 0.863)	0.853 (0.72; 0.927)	0.608 (0.492; 0.74)	0.597 (0.468; 0.69)	0.749 (0.634; 0.845)	0.869 (0.783; 0.942)	0.709 (0.627; 0.834)
post_ltx vs healthy genus terminal_ileum	0.892 (0.841; 0.962)	0.853 (0.72; 0.927)	0.853 (0.72; 0.927)	0.781 (0.547; 0.871)	0.71 (0.571; 0.796)	0.869 (0.783; 0.942)	0.869 (0.783; 0.942)	0.955 (0.924; 0.993)

ROC - ASV

rocs_list <- supplements_models$roc_cs
rocs_list[["enet_model"]] <- roc_cs

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(1:3)],
                       Q="Q1_czech",
                       model_name=model_name)
}

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

ROC - genus

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(4:6)],
                       Q="Q1_czech",
                       model_name=model_name)
}

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

Analysis - Colon

segment="colon"

Filtering

Rules: - prevalence > 5% (per group) - nearZeroVar with default settings - sequencing depth > 5000 - taxonomic assignment at least order

Library size

read_counts(colon_asv_tab, line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Sequencing depth

data_filt <- seq_depth_filtering(colon_asv_tab,
                                 colon_taxa_tab,
                                 colon_metadata,
                                 seq_depth_threshold = 10000)

## Removing 12 ASV(s)

filt_colon_asv_tab <- data_filt[[1]]; alpha_colon_asv_tab <- filt_colon_asv_tab
filt_colon_taxa_tab <- data_filt[[2]]; alpha_colon_taxa_tab <- filt_colon_taxa_tab
filt_colon_metadata <- data_filt[[3]]; alpha_colon_metadata <- filt_colon_metadata

seq_step <- dim(filt_colon_asv_tab)[1]

Library size

read_counts(filt_colon_asv_tab,line = c(10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

NearZeroVar

data_filt <- nearzerovar_filtering(filt_colon_asv_tab,
                                   filt_colon_taxa_tab,
                                   filt_colon_metadata)

filt_colon_asv_tab <- data_filt[[1]]
filt_colon_taxa_tab <- data_filt[[2]]
nearzero_step <- dim(filt_colon_asv_tab)[1]

Library size

read_counts(filt_colon_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Check zero depth

data_filt <- check_zero_depth(filt_colon_asv_tab, 
                              filt_colon_taxa_tab, 
                              filt_colon_metadata)

filt_colon_asv_tab <- data_filt[[1]]; 
filt_colon_taxa_tab <- data_filt[[2]]; 
filt_colon_metadata <- data_filt[[3]]; 

Library size

read_counts(filt_colon_asv_tab,line = c(5000,10000))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

Final Counts

final_counts_filtering(colon_asv_tab,
                       filt_colon_asv_tab,
                       filt_colon_metadata,
                       seq_step, 0, nearzero_step)

Alpha diversity

path = "../results/Q1_czech/alpha_diversity"

Calculation

# Construct MPSE object
alpha_colon_metadata$Sample <- alpha_colon_metadata$SampleID
colon_mpse <- as.MPSE(construct_phyloseq(alpha_colon_asv_tab,
                                         alpha_colon_taxa_tab,
                                         alpha_colon_metadata))

colon_mpse %<>% mp_rrarefy(raresize = 10000,seed = 123)

# Calculate alpha diversity - rarefied counts
colon_mpse %<>% mp_cal_alpha(.abundance=RareAbundance, force=TRUE)

alpha_data <- data.frame(SampleID=colon_mpse$Sample.x,
                         Observe=colon_mpse$Observe,
                         Shannon=colon_mpse$Shannon,
                         Simpson=colon_mpse$Simpson,
                         Pielou=colon_mpse$Pielou,
                         Group=colon_mpse$Group,
                         Country=colon_mpse$Country,
                         Patient=colon_mpse$Patient)

write.csv(alpha_data,file.path(path,paste0("alpha_indices_",segment,".csv")),
          row.names = FALSE)

Plots

Custom plot

alpha_data <- alpha_data %>% 
  dplyr::select(-c("Simpson","Pielou")) %>%
  mutate(Richness=Observe)

p_B <- alpha_diversity_custom_2(alpha_data,
                                size = 1.5,
                                width = 0.3)

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

# save the results
alpha_div_plots[[paste(segment,"Custom")]] <- p_B

p_B

Linear Model

path = "../results/Q1_czech/alpha_diversity"
alpha_data <- read.csv(file.path(path,paste0("alpha_indices_",segment,".csv")))

Richness

results_model <- pairwise.lmer(
  formula = "Observe ~ Group + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_observe <- results_model[[1]]
  results_model_observe_detailed <- results_model[[2]]
} else {
  results_model_observe <- results_model
  results_model_observe_detailed <- NA
}

# save the results
pc_observed[[segment]] <- results_model_observe

# see the results
knitr::kable(results_model_observe,digits = 3,
caption = "Raw results of linear model of richness estimation.")

Raw results of linear model of richness estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-56.074	12.083	77.355	-4.641	0.000	0.000	***
pre_ltx vs Grouppost_ltx	28.874	12.150	133.073	2.377	0.019	0.019	*
healthy vs Grouppost_ltx	-27.195	8.555	161.359	-3.179	0.002	0.003	**

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	df	t value	Pr(>|t|)
healthy	171.741	6.999	162.573	24.537	0
pre_ltx	115.661	10.965	133.827	10.549	0
healthy	171.795	6.790	77.014	25.302	0

Shannon

results_model <- pairwise.lmer(
  formula = "Shannon ~ Group + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_shannon <- results_model[[1]]
  results_model_shannon_detailed <- results_model[[2]]
} else {
  results_model_shannon <- results_model
  results_model_shannon_detailed <- NA
}

# save the results
pc_shannon[[segment]] <- as.data.frame(results_model_shannon)

# see the results
knitr::kable(results_model_shannon,digits = 3,
caption = "Raw results of linear model of Shannon estimation.")

Raw results of linear model of Shannon estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.585	0.134	77.248	-4.359	0.000	0.00	***
pre_ltx vs Grouppost_ltx	0.365	0.160	130.742	2.281	0.024	0.03	*
healthy vs Grouppost_ltx	-0.220	0.101	158.220	-2.185	0.030	0.03	*

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	df	t value	Pr(>|t|)
healthy	3.686	0.082	159.083	44.800	0
pre_ltx	3.101	0.144	131.102	21.518	0
healthy	3.686	0.075	76.839	48.853	0

Simpson

results_model <- pairwise.lmer(
  formula = "Simpson ~ Group + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_simpson <- results_model[[1]]
  results_model_simpson_detailed <- results_model[[2]]
} else {
  results_model_simpson <- results_model
  results_model_simpson_detailed <- NA
}

# save the results
pc_simpson[[segment]] <- as.data.frame(results_model_simpson)

# see the results
knitr::kable(results_model_simpson,digits = 3,
caption = "Raw results of linear model of Simpson estimation.")

Raw results of linear model of Simpson estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.059	0.016	78.192	-3.658	0.000	0.001	**
pre_ltx vs Grouppost_ltx	0.031	0.027	130.285	1.174	0.242	0.242
healthy vs Grouppost_ltx	-0.027	0.017	157.776	-1.613	0.109	0.163

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	df	t value	Pr(>|t|)
healthy	0.944	0.014	158.265	68.189	0
pre_ltx	0.885	0.024	130.534	36.671	0
healthy	0.944	0.009	77.739	104.354	0

Pielou

results_model <- pairwise.lmer(
  formula = "Pielou ~ Group + (1|Patient)",
  factors=alpha_data$Group,
  data=alpha_data)

# check interaction
if (!is.data.frame(results_model)){
  results_model_pielou <- results_model[[1]]
  results_model_pielou_detailed <- results_model[[2]]
} else {
  results_model_pielou <- results_model
  results_model_pielou_detailed <- NA
}

# save the results
pc_pielou[[segment]] <- as.data.frame(results_model_pielou)

# see the results
knitr::kable(results_model_pielou,digits = 3,
caption = "Raw results of linear model of Pielou estimation.")

Raw results of linear model of Pielou estimation.

	Estimate	Std..Error	df	t.value	Pr…t..	p.adj	sig
healthy vs Grouppre_ltx	-0.057	0.018	77.904	-3.176	0.002	0.006	**
pre_ltx vs Grouppost_ltx	0.039	0.025	130.452	1.603	0.111	0.167
healthy vs Grouppost_ltx	-0.018	0.016	157.415	-1.139	0.257	0.257

# see the results
knitr::kable(results_model[[3]],digits = 3,
caption = "Group means")

Group means

	Estimate	Std. Error	df	t value	Pr(>|t|)
healthy	0.721	0.013	158.282	55.948	0
pre_ltx	0.663	0.022	130.843	29.919	0
healthy	0.721	0.010	77.363	71.018	0

Saving results

alpha_list <- list(
  Richness=pc_observed[[segment]] %>% rownames_to_column("Comparison"),
  Shannon=pc_shannon[[segment]] %>% rownames_to_column("Comparison"),
  Simpson=pc_simpson[[segment]] %>% rownames_to_column("Comparison"),
  Pielou=pc_pielou[[segment]] %>% rownames_to_column("Comparison"))
                   
write.xlsx(alpha_list, 
           file = file.path(path,paste0("alpha_diversity_results_",segment,".xlsx")))

Beta diversity

Calculating Aitchison distance (euclidean distance on clr-transformed data), both at ASV and genus level.

Main analysis - Genus, Aitchison

Genus level, Aitchison distance

level="genus"

path = "../results/Q1_czech/beta_diversity"

Aggregation, filtering

# Aggregation
genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level=level,
                             names=TRUE)
# Filtration
filt_data <- filtering_steps(genus_data[[1]],
                             genus_data[[2]],
                             colon_metadata,
                             seq_depth_threshold=10000)

filt_colon_genus_tab <- filt_data[[1]]
filt_colon_genus_taxa <- filt_data[[2]]
filt_colon_genus_metadata <- filt_data[[3]]

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_genus_metadata$Group,
                           covariate = NULL, 
                           patients = filt_colon_genus_metadata$Patient,
                           sim.method = "robust.aitchison", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
pairwise_aitchison_raw[[paste(level, segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	370.737	2.136	0.016	0.217	0.217
pre_ltx vs post_ltx	1	394.216	2.228	0.009	0.103	0.154
post_ltx vs healthy	1	904.473	5.126	0.017	0.001	0.003	**

Plots

Custom

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
pca_plots_list[[paste(segment,level,"custom")]] <- p

# see the results
p

Saving results

write.xlsx(pairwise_aitchison_raw[[paste(level, segment)]], 
           file = file.path(path,
           paste0("beta_diversity_results_", segment,".xlsx")))

Supplementary analysis

Genus level

level="genus"

Bray-Curtis

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_genus_metadata$Group,
                           covariate = NULL, 
                            patients = filt_colon_genus_metadata$Patient,
                           sim.method = "bray", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.668	3.662	0.026	0.053	0.053
pre_ltx vs post_ltx	1	0.700	3.489	0.014	0.014	0.021	*
post_ltx vs healthy	1	1.306	7.046	0.023	0.001	0.003	**

Plots

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 measure = "bray",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

pairwise_df <- filt_colon_genus_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_genus_metadata$Group,
                           covariate = NULL, 
                            patients = filt_colon_genus_metadata$Patient,
                           sim.method = "jaccard", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.752	2.816	0.020	0.067	0.067
pre_ltx vs post_ltx	1	0.830	2.894	0.011	0.022	0.033	*
post_ltx vs healthy	1	1.461	5.331	0.017	0.001	0.003	**

Plots

Custom

p <- pca_plot_custom(filt_colon_genus_tab,
                                 filt_colon_genus_taxa,
                                 filt_colon_genus_metadata,
                                 measure = "jaccard",
                                 show_boxplots = TRUE,
                                 variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

ASV level

level="ASV"

Aitchison

PERMANOVA

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_metadata$Group,
                           covariate = NULL, 
                            patients = filt_colon_metadata$Patient,
                           sim.method = "robust.aitchison", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("aitchison",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	453.896	1.573	0.012	0.779	0.779
pre_ltx vs post_ltx	1	515.107	1.889	0.008	0.323	0.485
post_ltx vs healthy	1	1332.113	4.762	0.016	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                           filt_colon_taxa_tab,
                           filt_colon_metadata,
                           show_boxplots = TRUE,
                           variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA aitchison",level,segment)]] <- p

# see the results
p

Bray-Curtis

PERMANOVA

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_metadata$Group,
                           covariate = NULL, 
                           patients = filt_colon_metadata$Patient,
                           sim.method = "bray", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("bray",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.703	2.227	0.016	0.247	0.247
pre_ltx vs post_ltx	1	0.940	2.914	0.012	0.022	0.033	*
post_ltx vs healthy	1	2.080	6.653	0.022	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                     filt_colon_taxa_tab,
                     filt_colon_metadata,
                     measure = "bray",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA bray",level,segment)]] <- p

# see the results
p

Jaccard

PERMANOVA

# preparing data frame
pairwise_df <- filt_colon_asv_tab %>% column_to_rownames("SeqID") %>% t()

# main effect
pp_main <- pairwise.adonis(pairwise_df,filt_colon_metadata$Group,
                           covariate = NULL, 
                           patients = filt_colon_metadata$Patient,
                           sim.method = "jaccard", p.adjust.m="BH")


# tidy the results
pp_factor <- pp_main

cols <- c("pairs","Df","SumsOfSqs", "F.Model","R2","p.value", "p.adjusted", "sig")
colnames(pp_factor) <- cols

# save raw results
supplements_beta[[paste("jaccard",level,segment)]] <- pp_factor

# see the results
knitr::kable(pp_factor,digits = 3,caption = "PERMANOVA, GROUP separation")

PERMANOVA, GROUP separation

pairs	Df	SumsOfSqs	F.Model	R2	p.value	p.adjusted	sig
pre_ltx vs healthy	1	0.681	1.767	0.013	0.632	0.632
pre_ltx vs post_ltx	1	0.888	2.272	0.009	0.073	0.109
post_ltx vs healthy	1	1.733	4.508	0.015	0.001	0.003	**

PCoA

p <- pca_plot_custom(filt_colon_asv_tab,
                     filt_colon_taxa_tab,
                     filt_colon_metadata,
                     measure = "jaccard",
                     show_boxplots = TRUE,
                     variable = "Group", size=3, show_legend=TRUE)

# save the results
supplements_beta[[paste("PCoA jaccard",level,segment)]] <- p

# see the results
p

Saving results

write.xlsx(supplements_beta[!grepl("PCoA",names(supplements_beta))],
           file = file.path(path,
           paste0("supplements_beta_diversity_", segment,".xlsx")))

Univariate Analysis

Main - Genus level

level="genus"

# needed paths
path = "../results/Q1_czech/univariate_analysis"
path_maaslin=file.path("../intermediate_files/maaslin/Q1_czech/",level)

# variables
raw_linda_results_genus[[segment]] <- list()
linda_results_genus[[segment]] <- list()

# country and interaction problems
list_country_union <- list()
list_intersections <- list()
list_venns <- list()
uni_statistics <- list()

# workbook for final df
wb <- createWorkbook()

# PSC effect
psc_effect <- list()

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

colon_genus_asv_taxa_tab <- create_asv_taxa_table(colon_genus_tab,
                                                  colon_genus_taxa_tab)

pre_ltx vs healthy

linDA

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 148 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  137  samples and  191  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])


for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))


volcano <- ggarrange(volcano_1)

# see the plot
volcano

## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))


volcano <- ggarrange(volcano1)

## Warning: Removed 18 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 17 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  251  samples and  181  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])


for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))

volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))


volcano <- ggarrange(volcano1)

## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, 
                                           list_intersections, 
                                           list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_genus_tab,
                            colon_genus_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 5 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data,
                   filt_colon_uni_metadata,
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  304  samples and  179  features will be tested!
## Pseudo-count approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

# save the results
group1 <- paste0(group[1], " vs ","Group",group[2])


for (grp in c(group1)){
  raw_linda_results_genus[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results_genus[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1, 
                                taxa_table = filt_colon_uni_taxa) + 
            ggtitle(paste(group,collapse=" vs "))

volcano <- ggarrange(volcano_1)

# see the plot
volcano

## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)

## Warning: Removed 30 rows containing missing values or values outside the scale range (`geom_text_repel()`).

volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results_genus, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results_genus[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- comparison_name
addWorksheet(wb, sheetName = new_name)
writeData(wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,
                                      uni_df,
                                      colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -1.88582 
## max_clr 7.041304 
## min_log -4.098288 
## max_log 4.831992

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(wb,file.path(path,paste0("uni_analysis_wb_",segment,".xlsx")),
             overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect[[paste(segment,level)]],file.path(path,paste0("psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa

write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("significant_taxa_",segment,".xlsx")))

Supplementary Analysis

ASV level

level="ASV"

path_maaslin="../intermediate_files/maaslin/Q1_czech/ASV/"

raw_linda_results[[segment]] <- list()
linda_results[[segment]] <- list()
supplements_wb <- createWorkbook()

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,
                            colon_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  137  samples and  518  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))


volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

Volcano plot

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)

# see the results
volcano

## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                                                                      segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,
                            colon_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 743 ASV(s)

## Removing 13 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  250  samples and  419  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)

# see the results
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment=segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_asv_tab,
                            colon_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 232 ASV(s)

## Removing 4 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  303  samples and  440  features will be tested!
## Pseudo-count approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))


volcano <- ggarrange(volcano_1)

# see the plot
volcano

## Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider increasing max.overlaps

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)
volcano

## Warning: ggrepel: 94 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,uni_df,colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -1.032666 
## max_clr 6.145786 
## min_log -4.652855 
## max_log 4.192808

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Phylum level

level="phylum"

path_maaslin="../intermediate_files/maaslin/Q1_czech/Phylum/"

raw_linda_results_phylum[[segment]] <- list()
linda_results_phylum[[segment]] <- list()

Aggregate taxa

phylum_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = "Phylum")

colon_phylum_tab <- phylum_data[[1]]
colon_phylum_taxa_tab <- phylum_data[[2]]

pre_ltx vs healthy

group <- c("healthy","pre_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

## Removing 7 ASV(s)

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  137  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

Volcano plot

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)

# see the results
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                                                                      segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "pre_ltx"
## [1] "healthy"
## [1] "pre_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  251  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))

volcano <- ggarrange(volcano1)

# see the results
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment=segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "pre_ltx"
## [1] "post_ltx"
## [1] "pre_ltx"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

post_ltx vs healthy

group <- c("healthy","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

linDA

# prepare the data
linda_data <- binomial_prep(colon_phylum_tab,
                            colon_phylum_taxa_tab,
                            colon_metadata,
                            group, usage="linDA")

filt_colon_uni_data <- linda_data[[1]]
filt_colon_uni_taxa <- linda_data[[2]]
filt_colon_uni_metadata <- linda_data[[3]]

# fit the model
linda.obj <- linda(filt_colon_uni_data, 
                   filt_colon_uni_metadata, 
                   formula = '~ Group + (1|Patient)')

## 0  features are filtered!
## The filtered data has  304  samples and  10  features will be tested!
## Imputation approach is used.
## Fit linear mixed effects models ...
## Completed.

linda.output <- linda.obj$output
linda.output <- linda_renaming(linda.output, group)

group1 <- paste0(group[1], " vs ","Group",group[2])

for (grp in c(group1)){
  raw_linda_results[[segment]][[grp]] <- 
    rawlinda.df(linda.output,
                grp,
                filt_colon_uni_data,
                filt_colon_uni_taxa)
  
  linda_results[[segment]][[grp]] <- 
    linda.df(linda.output,
             grp,
             filt_colon_uni_data,
             filt_colon_uni_taxa)
}

# volcano plot
volcano_1 <- volcano_plot_linda(linda.output, group1,
                                taxa_table = filt_colon_uni_taxa) +
              ggtitle(paste(group,collapse=" vs "))

## Using Phylum for naming

volcano <- ggarrange(volcano_1)

# see the plot
volcano

MaAsLin2

volcano1 <- volcano_plot_maaslin(fit_data,filt_colon_uni_taxa) + 
            ggtitle(paste(group[1], "vs", group[2]))


volcano <- ggarrange(volcano1)
volcano

Group - Intersection

intersection_results <- group_intersection(group, list_intersections, list_venns,
                                           linda.output, fit_data,
                                           raw_linda_results, 
                                           segment = segment,
                                           level=level)

list_intersections <- intersection_results[[1]]
list_venns <- intersection_results[[2]]
venn <- intersection_results[[3]]

# show the results
venn

Basic statistics

uni_df <-  merge(basic_univariate_statistics(linda_data,group),
                 raw_linda_results[[segment]][[group1]],
                 by="SeqID",all=TRUE)

## [1] "healthy"
## [1] "post_ltx"
## [1] "healthy"
## [1] "post_ltx"

uni_df[["final_sig"]] <- uni_df$SeqID %in% list_intersections[[paste(segment,level,comparison_name)]][["SeqID"]]
uni_statistics[[segment]][[paste(level,comparison_name)]] <- uni_df

# for comparison
new_name <- paste(level,comparison_name)
addWorksheet(supplements_wb, sheetName = new_name)
writeData(supplements_wb, sheet = new_name, uni_df, rowNames=FALSE)

Visualization

Heatmap visualizing the linDA’s logFoldChange for taxa with p < 0.1.

list_heatmap <- list_intersections[grep(paste(segment,level),
                                  names(list_intersections),value=TRUE)]

p_heatmap_linda <- heatmap_linda(list_heatmap,colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## Warning: The dcast generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. Please do this redirection yourself like
## reshape2::dcast(plot_df_combined). In the next version, this warning will become an error.

p_heatmap_linda

Dot heatmap

dotheatmap_linda <- dot_heatmap_linda(list_heatmap,uni_df,colon_taxa_tab)

## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.
## Warning: The melt generic in data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and
## this redirection is now deprecated. To continue using melt methods from reshape2 while both libraries are
## attached, e.g. melt.list, you can prepend the namespace, i.e. reshape2::melt(plot_df). In the next version,
## this warning will become an error.

## min_clr -3.538652 
## max_clr 4.750646 
## min_log -3.526691 
## max_log 2.048554

dotheatmap_linda

PSC effect

pre_LTx vs Healthy and Post_LTx vs Healthy intersection

imagename

A <- list_intersections[[paste(segment,level,"healthy vs pre_ltx")]]$SeqID
B <- list_intersections[[paste(segment,level,"healthy vs post_ltx")]]$SeqID
C <- intersect(A,B)

psc_effect[[paste(segment,level)]] <- list_intersections[[paste(segment,level, "healthy vs pre_ltx")]][list_intersections[[paste(segment,level, "healthy vs pre_ltx")]]$SeqID %in% C,]
  
# see the results
psc_effect[[paste(segment,level)]] 

Saving results

# ALL DATA
saveWorkbook(supplements_wb,file.path(path,paste0("supplements_uni_analysis_wb_",segment,".xlsx")),overwrite = TRUE)

# PSC effect
write.xlsx(psc_effect,
          file.path(path,paste0("supplements_psc_effect_",segment,".xlsx")))

# SIGNIFICANT taxa
write.xlsx(list_intersections[grepl(segment,names(list_intersections))] %>%
            `names<-`(gsub(segment, "", names(
              list_intersections[grepl(segment,names(list_intersections))]))),
           file.path(path,paste0("supplements_significant_taxa_",segment,".xlsx")))

Machine learning

path = "../results/Q1_czech/models"

ElasticNet

model="enet"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.07726996
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.92487601
## auc_optimism_corrected_CIL      0.83593690
## auc_optimism_corrected_CIU      0.97978125
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.87779018
## accuracy_optimism_corrected_CIL 0.79942979
## accuracy_optimism_corrected_CIU 0.95779630

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 95  0
##    1  0 42
## 
## $czech
##     Predicted
## True  0  1
##    0 95  0
##    1  0 42
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           1.000000000
## lambda                          0.007559533
## auc                             1.000000000
## auc_czech                       1.000000000
## auc_no                                  NaN
## auc_optimism_corrected          0.845603174
## auc_optimism_corrected_CIL      0.732635525
## auc_optimism_corrected_CIU      0.914518784
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                             NaN
## accuracy_optimism_corrected     0.840254112
## accuracy_optimism_corrected_CIL 0.781520563
## accuracy_optimism_corrected_CIU 0.897722491

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 208   0
##    1   0  42
## 
## $czech
##     Predicted
## True   0   1
##    0 208   0
##    1   0  42
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.400000000
## lambda                          0.008015337
## auc                             1.000000000
## auc_czech                       1.000000000
## auc_no                                  NaN
## auc_optimism_corrected          0.976476725
## auc_optimism_corrected_CIL      0.958769096
## auc_optimism_corrected_CIU      0.990150128
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                             NaN
## accuracy_optimism_corrected     0.921683460
## accuracy_optimism_corrected_CIL 0.866156635
## accuracy_optimism_corrected_CIU 0.969726503

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  95   0
##    1   0 208
## 
## $czech
##     Predicted
## True   0   1
##    0  95   0
##    1   0 208
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           1.00000000
## lambda                          0.01369607
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.94053612
## auc_optimism_corrected_CIL      0.90046278
## auc_optimism_corrected_CIU      0.98930682
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.84937424
## accuracy_optimism_corrected_CIL 0.76972516
## accuracy_optimism_corrected_CIU 0.93963675

enet_model$conf_matrices

## $original
##     Predicted
## True  0  1
##    0 95  0
##    1  0 42
## 
## $czech
##     Predicted
## True  0  1
##    0 95  0
##    1  0 42
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.00000000
## lambda                          0.04874797
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.75472529
## auc_optimism_corrected_CIL      0.54235491
## auc_optimism_corrected_CIU      0.83904574
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.81716332
## accuracy_optimism_corrected_CIL 0.76429070
## accuracy_optimism_corrected_CIU 0.85651042

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0 209   0
##    1   0  42
## 
## $czech
##     Predicted
## True   0   1
##    0 209   0
##    1   0  42
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group",
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
models_summ[[model_name]] <- enet_model$model_summary
models_cm[[model_name]] <- enet_model$conf_matrices$original
roc_cs[[model_name]] <- enet_model$kfold_rocobjs
betas[[model_name]] <- as.matrix(enet_model$betas)

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.00352282
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.97314964
## auc_optimism_corrected_CIL      0.95668510
## auc_optimism_corrected_CIU      0.98693210
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.91758825
## accuracy_optimism_corrected_CIL 0.88106563
## accuracy_optimism_corrected_CIU 0.94831081

enet_model$conf_matrices

## $original
##     Predicted
## True   0   1
##    0  95   0
##    1   0 209
## 
## $czech
##     Predicted
## True   0   1
##    0  95   0
##    1   0 209
## 
## $no
## [1] NaN

enet_model$plot

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

roc_c

Saving results

models_summ_df_colon <- do.call(rbind, 
  models_summ[grep(segment,names(models_summ),value = TRUE)])

write.csv(models_summ_df_colon,file.path(path,paste0("elastic_net_",segment,".csv")))

Supplementary models

CLR-transformed data

kNN

model="knn"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9740602
## auc_optimism_corrected           0.9085611
## auc_optimism_corrected_CIL       0.8300600
## auc_optimism_corrected_CIU       0.9923750
## accuracy                         0.9270073
## accuracy_optimism_corrected      0.8742371
## accuracy_optimism_corrected_CIL  0.7918421
## accuracy_optimism_corrected_CIU  0.9439637

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9285714
## auc_optimism_corrected           0.7688393
## auc_optimism_corrected_CIL       0.6323093
## auc_optimism_corrected_CIU       0.8526483
## accuracy                         0.8840000
## accuracy_optimism_corrected      0.7978995
## accuracy_optimism_corrected_CIL  0.7454545
## accuracy_optimism_corrected_CIU  0.8586340

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               30.0000000
## auc                              0.9594383
## auc_optimism_corrected           0.8765328
## auc_optimism_corrected_CIL       0.8292319
## auc_optimism_corrected_CIU       0.9283138
## accuracy                         0.9009901
## accuracy_optimism_corrected      0.7962614
## accuracy_optimism_corrected_CIL  0.7160377
## accuracy_optimism_corrected_CIU  0.8768596

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9761905
## auc_optimism_corrected           0.8510016
## auc_optimism_corrected_CIL       0.7579809
## auc_optimism_corrected_CIU       0.9430530
## accuracy                         0.8467153
## accuracy_optimism_corrected      0.7932624
## accuracy_optimism_corrected_CIL  0.7231440
## accuracy_optimism_corrected_CIU  0.9186564

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9276601
## auc_optimism_corrected           0.6952586
## auc_optimism_corrected_CIL       0.5157044
## auc_optimism_corrected_CIU       0.8371418
## accuracy                         0.8725100
## accuracy_optimism_corrected      0.8041781
## accuracy_optimism_corrected_CIL  0.7435581
## accuracy_optimism_corrected_CIU  0.8781787

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model"]][[model_name]] <- knn_model$model_summary
supplements_models[["roc_cs"]][["knn_model"]][[model_name]] <- knn_model$kfold_rocobjs


# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               30.0000000
## auc                              0.9755981
## auc_optimism_corrected           0.8877868
## auc_optimism_corrected_CIL       0.8342668
## auc_optimism_corrected_CIU       0.9326126
## accuracy                         0.8026316
## accuracy_optimism_corrected      0.7431760
## accuracy_optimism_corrected_CIL  0.6842593
## accuracy_optimism_corrected_CIU  0.7972124

roc_c

Random Forest

model="rf"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "73"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.9241346"
## auc_optimism_corrected_CIL      "0.8647446"
## auc_optimism_corrected_CIU      "0.9664063"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8123775"
## accuracy_optimism_corrected_CIL "0.7444715"
## accuracy_optimism_corrected_CIU "0.9066061"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "225"      
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.7490965"
## auc_optimism_corrected_CIL      "0.5914776"
## auc_optimism_corrected_CIU      "0.8699399"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8050533"
## accuracy_optimism_corrected_CIL "0.7619048"
## accuracy_optimism_corrected_CIU "0.8537056"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "99"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9365846"
## auc_optimism_corrected_CIL      "0.8922044"
## auc_optimism_corrected_CIU      "0.980926" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8948373"
## accuracy_optimism_corrected_CIL "0.8345397"
## accuracy_optimism_corrected_CIU "0.9444"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "163"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9259073"
## auc_optimism_corrected_CIL      "0.8316926"
## auc_optimism_corrected_CIU      "0.9934783"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8508802"
## accuracy_optimism_corrected_CIL "0.7838161"
## accuracy_optimism_corrected_CIU "0.9338652"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "177"      
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.6897877"
## auc_optimism_corrected_CIL      "0.5414078"
## auc_optimism_corrected_CIU      "0.773423" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8044215"
## accuracy_optimism_corrected_CIL "0.7520434"
## accuracy_optimism_corrected_CIU "0.8841924"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model"]][[model_name]] <- rf_model$model_summary
supplements_models[["roc_cs"]][["rf_model"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

Gradient boosting

model="gb"

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_clr",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs


# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_clr",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model"]][[model_name]] <- gbm_model$model_summary
supplements_models[["roc_cs"]][["gbm_model"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "17"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9550928"
## auc_optimism_corrected_CIL      "0.9139744"
## auc_optimism_corrected_CIU      "0.9872226"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8980211"
## accuracy_optimism_corrected_CIL "0.860119" 
## accuracy_optimism_corrected_CIU "0.9328664"

roc_c

Relative abundances

Elastic net

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.05188328
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.89858321
## auc_optimism_corrected_CIL      0.76586912
## auc_optimism_corrected_CIU      0.97379261
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.83973932
## accuracy_optimism_corrected_CIL 0.77165385
## accuracy_optimism_corrected_CIU 0.93386525

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.60000000
## lambda                          0.01941712
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.66876106
## auc_optimism_corrected_CIL      0.50000000
## auc_optimism_corrected_CIU      0.82942892
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.80901776
## accuracy_optimism_corrected_CIL 0.76190476
## accuracy_optimism_corrected_CIU 0.86240006

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.80000000
## lambda                          0.00683854
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.89770344
## auc_optimism_corrected_CIL      0.82355908
## auc_optimism_corrected_CIU      0.96320425
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.82505124
## accuracy_optimism_corrected_CIL 0.72905487
## accuracy_optimism_corrected_CIU 0.89347623

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.20000000
## lambda                          0.04255809
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.88942146
## auc_optimism_corrected_CIL      0.75637679
## auc_optimism_corrected_CIU      0.98956522
## accuracy                        1.00000000
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.83286249
## accuracy_optimism_corrected_CIL 0.75306818
## accuracy_optimism_corrected_CIU 0.91865642

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)


# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                       [,1]
## alpha                           0.00000000
## lambda                          0.08050446
## auc                             1.00000000
## auc_czech                       1.00000000
## auc_no                                 NaN
## auc_optimism_corrected          0.61705894
## auc_optimism_corrected_CIL      0.50044118
## auc_optimism_corrected_CIU      0.77259688
## accuracy                        0.98007968
## accuracy_czech                         NaN
## accuracy_no                            NaN
## accuracy_optimism_corrected     0.78668254
## accuracy_optimism_corrected_CIL 0.74549419
## accuracy_optimism_corrected_CIU 0.83364326

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
enet_model <- glmnet_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                              clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(enet_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["enet_model_ra"]][[model_name]] <- enet_model$model_summary

supplements_models[["roc_cs"]][["enet_model_ra"]][[model_name]] <- enet_model$kfold_rocobjs

# see the results
enet_model$model_summary %>% t()

##                                        [,1]
## alpha                           0.600000000
## lambda                          0.005060013
## auc                             1.000000000
## auc_czech                       1.000000000
## auc_no                                  NaN
## auc_optimism_corrected          0.908839490
## auc_optimism_corrected_CIL      0.879280481
## auc_optimism_corrected_CIU      0.949067862
## accuracy                        1.000000000
## accuracy_czech                          NaN
## accuracy_no                             NaN
## accuracy_optimism_corrected     0.848866015
## accuracy_optimism_corrected_CIL 0.816775599
## accuracy_optimism_corrected_CIU 0.879510008

roc_c

kNN

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9296992
## auc_optimism_corrected           0.6479469
## auc_optimism_corrected_CIL       0.4585905
## auc_optimism_corrected_CIU       0.8331452
## accuracy                         0.7153285
## accuracy_optimism_corrected      0.6797730
## accuracy_optimism_corrected_CIL  0.5847160
## accuracy_optimism_corrected_CIU  0.8562678

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               30.0000000
## auc                              0.8895375
## auc_optimism_corrected           0.6464599
## auc_optimism_corrected_CIL       0.4859681
## auc_optimism_corrected_CIU       0.7592457
## accuracy                         0.8320000
## accuracy_optimism_corrected      0.8058950
## accuracy_optimism_corrected_CIL  0.7619048
## accuracy_optimism_corrected_CIU  0.8506443

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               28.0000000
## auc                              0.8506832
## auc_optimism_corrected           0.7208749
## auc_optimism_corrected_CIL       0.6189545
## auc_optimism_corrected_CIU       0.8390812
## accuracy                         0.7953795
## accuracy_optimism_corrected      0.6946665
## accuracy_optimism_corrected_CIL  0.6099932
## accuracy_optimism_corrected_CIU  0.7814890

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9432331
## auc_optimism_corrected           0.6025594
## auc_optimism_corrected_CIL       0.4761007
## auc_optimism_corrected_CIU       0.7437072
## accuracy                         0.7226277
## accuracy_optimism_corrected      0.6855019
## accuracy_optimism_corrected_CIL  0.6009375
## accuracy_optimism_corrected_CIU  0.8623397

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               10.0000000
## auc                              0.9046480
## auc_optimism_corrected           0.6067022
## auc_optimism_corrected_CIL       0.4877012
## auc_optimism_corrected_CIU       0.6966009
## accuracy                         0.8406375
## accuracy_optimism_corrected      0.7874867
## accuracy_optimism_corrected_CIL  0.7561442
## accuracy_optimism_corrected_CIU  0.8230462

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
knn_model <- knn_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(knn_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["knn_model_ra"]][[model_name]] <- knn_model$model_summary

supplements_models[["roc_cs"]][["knn_model_ra"]][[model_name]] <- knn_model$kfold_rocobjs

# see the results
knn_model$model_summary %>% t()

##                                       [,1]
## k                               11.0000000
## auc                              0.9049106
## auc_optimism_corrected           0.7556965
## auc_optimism_corrected_CIL       0.6097428
## auc_optimism_corrected_CIU       0.8283564
## accuracy                         0.8618421
## accuracy_optimism_corrected      0.7102348
## accuracy_optimism_corrected_CIL  0.5993561
## accuracy_optimism_corrected_CIU  0.7815629

roc_c

Random Forest

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs

# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "81"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.943333" 
## auc_optimism_corrected_CIL      "0.8778251"
## auc_optimism_corrected_CIU      "0.995"    
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8572153"
## accuracy_optimism_corrected_CIL "0.7968816"
## accuracy_optimism_corrected_CIU "0.9200926"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "91"       
## splitrule                       "gini"     
## min.node.size                   "5"        
## auc                             "1"        
## auc_optimism_corrected          "0.8150053"
## auc_optimism_corrected_CIL      "0.6932038"
## auc_optimism_corrected_CIU      "0.9355531"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8184509"
## accuracy_optimism_corrected_CIL "0.7672619"
## accuracy_optimism_corrected_CIU "0.8647197"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "23"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9610293"
## auc_optimism_corrected_CIL      "0.929291" 
## auc_optimism_corrected_CIU      "0.9849327"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8973151"
## accuracy_optimism_corrected_CIL "0.8246069"
## accuracy_optimism_corrected_CIU "0.934145"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "25"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9377114"
## auc_optimism_corrected_CIL      "0.8565378"
## auc_optimism_corrected_CIU      "0.9941304"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8483887"
## accuracy_optimism_corrected_CIL "0.7644926"
## accuracy_optimism_corrected_CIU "0.9396368"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "181"      
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.7142251"
## auc_optimism_corrected_CIL      "0.5346593"
## auc_optimism_corrected_CIU      "0.7945068"
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8053349"
## accuracy_optimism_corrected_CIL "0.7583286"
## accuracy_optimism_corrected_CIU "0.8795533"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
rf_model <- rf_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                        clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(rf_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["rf_model_ra"]][[model_name]] <- rf_model$model_summary

supplements_models[["roc_cs"]][["rf_model_ra"]][[model_name]] <- rf_model$kfold_rocobjs


# see the results
rf_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

Gradient boosting

ASV level

level="ASV"

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 1808 ASV(s)

## Removing 13 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 743 ASV(s)

## Removing 13 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_asv_tab,
                                     colon_taxa_tab,
                                     colon_metadata,
                                     group, usage="ml_ra",
                                     patient = TRUE)

## Removing 232 ASV(s)

## Removing 4 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

Genus level

level="genus"

Aggregate taxa

genus_data <- aggregate_taxa(colon_asv_tab,
                             colon_taxa_tab,
                             taxonomic_level = level)

colon_genus_tab <- genus_data[[1]]
colon_genus_taxa_tab <- genus_data[[2]]

pre_ltx vs healthy

group <- c("pre_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 148 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

pre_ltx vs post_ltx

group <- c("pre_ltx","post_ltx")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 17 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

post_ltx vs healthy

group <- c("post_ltx","healthy")
comparison_name <- paste0(group[1], " vs ",group[2])

model_name <- paste(comparison_name,level,segment)

# prepare the data
filt_colon_uni_data <- binomial_prep(colon_genus_tab,
                                     colon_genus_taxa_tab,
                                     colon_metadata,
                                     group, 
                                     usage="ml_ra",
                                     patient = TRUE)

## Removing 5 ASV(s)

# fit the model
gbm_model <- gbm_binomial(filt_colon_uni_data,
                              sample_method = "atypboot",
                              outcome="Group", 
                              N=10,
                          clust_var="Patient",
                              reuse=TRUE,
                              file=model_name,
                              Q="Q1_czech")

# ROC curve
roc_c <- roc_curve(gbm_model, group)

## Warning in geom_line(aes(x = `1-specificity`, y = sensitivity, by = name, : Ignoring unknown aesthetics: by

# save the results
supplements_models[["models_summ"]][["gbm_model_ra"]][[model_name]] <- gbm_model$model_summary

supplements_models[["roc_cs"]][["gbm_model_ra"]][[model_name]] <- gbm_model$kfold_rocobjs

# see the results
gbm_model$model_summary %>% t()

##                                 [,1]       
## mtry                            "55"       
## splitrule                       "gini"     
## min.node.size                   "2"        
## auc                             "1"        
## auc_optimism_corrected          "0.9587751"
## auc_optimism_corrected_CIL      "0.9118397"
## auc_optimism_corrected_CIU      "0.994497" 
## accuracy                        "1"        
## accuracy_optimism_corrected     "0.8863985"
## accuracy_optimism_corrected_CIL "0.8151464"
## accuracy_optimism_corrected_CIU "0.9170257"

roc_c

Saving results

models_list <- list()

for (model_name in names(supplements_models$models_summ)){
  df <- do.call(rbind, supplements_models$models_summ[[model_name]])
  models_list[[model_name]] <- df
}

write.xlsx(models_list,
           file=file.path(path,paste0("supplements_models_",segment,".xlsx")),
           rowNames=TRUE)

Results overview

Alpha diversity

pc_observed[[segment]]

pc_shannon[[segment]]

pc_simpson[[segment]]

pc_pielou[[segment]]

Plots

alpha_div_plots[[paste(segment,"Country")]]

## NULL

alpha_div_plots[[paste(segment,"Custom")]]

Beta diversity

Main results

pairwise_aitchison_raw[[paste("genus", segment)]]

PCA

pca_plots_list[[paste(segment,"genus custom")]]

Supplements

knitr::kable(supplements_beta[!grepl("PCoA",names(supplements_beta))],
             digits = 3,
             caption = "Supplementary PERMANOVA results")

Supplementary PERMANOVA results

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.412 2.198 0.036 0.008 0.012 * pre_ltx vs post_ltx 1 0.230 1.052 0.008 0.350 0.350 post_ltx vs healthy 1 0.771 3.752 0.027 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.499 1.835 0.030 0.005 0.007 ** pre_ltx vs post_ltx 1 0.333 1.101 0.009 0.262 0.262 post_ltx vs healthy 1 0.865 2.980 0.021 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 484.192 1.552 0.026 0.002 0.003 ** pre_ltx vs post_ltx 1 320.395 1.130 0.009 0.121 0.121 post_ltx vs healthy 1 877.994 2.987 0.021 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.566 1.748 0.029 0.007 0.011 * pre_ltx vs post_ltx 1 0.437 1.284 0.010 0.113 0.113 post_ltx vs healthy 1 1.212 3.689 0.026 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.558 1.423 0.024 0.008 0.012 * pre_ltx vs post_ltx 1 0.474 1.174 0.009 0.102 0.102 post_ltx vs healthy 1 1.027 2.597 0.019 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.668 3.662 0.026 0.053 0.053 pre_ltx vs post_ltx 1 0.700 3.489 0.014 0.014 0.021 * post_ltx vs healthy 1 1.306 7.046 0.023 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.752 2.816 0.020 0.067 0.067 pre_ltx vs post_ltx 1 0.830 2.894 0.011 0.022 0.033 * post_ltx vs healthy 1 1.461 5.331 0.017 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 453.896 1.573 0.012 0.779 0.779 pre_ltx vs post_ltx 1 515.107 1.889 0.008 0.323 0.485 post_ltx vs healthy 1 1332.113 4.762 0.016 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.703 2.227 0.016 0.247 0.247 pre_ltx vs post_ltx 1 0.940 2.914 0.012 0.022 0.033 * post_ltx vs healthy 1 2.080 6.653 0.022 0.001 0.003 **

pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig pre_ltx vs healthy 1 0.681 1.767 0.013 0.632 0.632 pre_ltx vs post_ltx 1 0.888 2.272 0.009 0.073 0.109 post_ltx vs healthy 1 1.733 4.508 0.015 0.001 0.003 **

PCA

plot_list <- supplements_beta[grepl("PCoA",names(supplements_beta)) &
                              grepl(segment,names(supplements_beta))]

ggarrange(plotlist = plot_list,
          labels=names(plot_list),
          font.label = list(size=5,face="plain"),
          ncol=2,nrow=3)

Univariate analysis

Number of significant taxa

knitr::kable(cbind(as.data.frame(lapply(list_intersections,nrow)),
      as.data.frame(lapply(psc_effect,nrow))) %>% t() %>% 
  `colnames<-`("Count") %>% 
  `rownames<-`(c(names(list_intersections),"PSC effect ASV","PSC effect Genus","PSC effect Phylum")),caption="Number of significant taxa")

Number of significant taxa

	Count
colon genus healthy vs pre_ltx	33
colon genus pre_ltx vs post_ltx	10
colon genus healthy vs post_ltx	46
colon ASV healthy vs pre_ltx	84
colon ASV pre_ltx vs post_ltx	6
colon ASV healthy vs post_ltx	105
colon phylum healthy vs pre_ltx	2
colon phylum pre_ltx vs post_ltx	3
colon phylum healthy vs post_ltx	5
PSC effect ASV	20
PSC effect Genus	52
PSC effect Phylum	2

Counts

# univar_list <- univariate_statistics(list_intersections,
#                                      psc_effect,
#                                      colon_genus_asv_taxa_tab)
# 
# univar_df <- univar_list[[1]]
# wb <- univar_list[[2]]
# 
# # save the results
# saveWorkbook(wb,"results/Q1/DAA_final_terminal_colon.xlsx", overwrite = TRUE)
# 
# # see the results
# univar_df

Machine learning

Main models

Summary

knitr::kable(models_summ_df_colon %>% dplyr::select(
"alpha","lambda",
"auc_optimism_corrected",
"auc_optimism_corrected_CIL",
"auc_optimism_corrected_CIU"),
             digits=3,caption="Elastic net results")

Elastic net results

	alpha	lambda	auc_optimism_corrected	auc_optimism_corrected_CIL	auc_optimism_corrected_CIU
pre_ltx vs healthy ASV colon	0.2	0.077	0.925	0.836	0.980
pre_ltx vs post_ltx ASV colon	1.0	0.008	0.846	0.733	0.915
post_ltx vs healthy ASV colon	0.4	0.008	0.976	0.959	0.990
pre_ltx vs healthy genus colon	1.0	0.014	0.941	0.900	0.989
pre_ltx vs post_ltx genus colon	0.0	0.049	0.755	0.542	0.839
post_ltx vs healthy genus colon	0.2	0.004	0.973	0.957	0.987

ROC - ASV level

roc_curve_all_custom(roc_cs[c(7:9)], 
                     Q="Q1_czech",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy ASV colon"  "pre_ltx vs post_ltx ASV colon" "post_ltx vs healthy ASV colon"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

ROC - Genus level

roc_curve_all_custom(roc_cs[c(10:12)],Q="Q1_czech",
                     model_name="enet_model")

## [1] "pre_ltx vs healthy genus colon"  "pre_ltx vs post_ltx genus colon" "post_ltx vs healthy genus colon"

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values

Supplementary models

Summary

# Build final dataframe
models_list[["enet_model"]] <- rbind(models_summ_df_colon,models_summ_df_colon)
final_df <- tibble(row_names = rownames(models_list[[1]]))

# Loop through models and extract required values
for (model_name in names(models_list)) {
  model_df <- models_list[[model_name]]
  
  # Combine AUC_optimism_corrected with its CI values
  final_df[[model_name]] <- paste0(
    round(model_df$auc_optimism_corrected, 3), 
    " (", round(model_df$auc_optimism_corrected_CIL, 3), "; ", 
    round(model_df$auc_optimism_corrected_CIU, 3), ")"
  )
}

knitr::kable(final_df, caption="All models")

All models

row_names	knn_model	rf_model	gbm_model	enet_model_ra	knn_model_ra	rf_model_ra	gbm_model_ra	enet_model
pre_ltx vs healthy ASV terminal_ileum	0.911 (0.846; 0.967)	0.829 (0.577; 0.984)	0.853 (0.72; 0.927)	0.845 (0.706; 0.944)	0.699 (0.52; 0.874)	0.917 (0.778; 0.984)	0.869 (0.783; 0.942)	0.925 (0.836; 0.98)
pre_ltx vs post_ltx ASV terminal_ileum	0.677 (0.585; 0.787)	0.656 (0.578; 0.735)	0.853 (0.72; 0.927)	0.565 (0.372; 0.822)	0.667 (0.574; 0.83)	0.794 (0.656; 0.887)	0.869 (0.783; 0.942)	0.846 (0.733; 0.915)
post_ltx vs healthy ASV terminal_ileum	0.818 (0.705; 0.952)	0.865 (0.747; 0.948)	0.853 (0.72; 0.927)	0.795 (0.708; 0.869)	0.704 (0.601; 0.791)	0.923 (0.852; 0.966)	0.869 (0.783; 0.942)	0.976 (0.959; 0.99)
pre_ltx vs healthy genus terminal_ileum	0.858 (0.791; 0.971)	0.882 (0.769; 0.963)	0.853 (0.72; 0.927)	0.851 (0.744; 0.983)	0.672 (0.456; 0.962)	0.922 (0.851; 0.976)	0.869 (0.783; 0.942)	0.941 (0.9; 0.989)
pre_ltx vs post_ltx genus terminal_ileum	0.693 (0.559; 0.805)	0.708 (0.503; 0.863)	0.853 (0.72; 0.927)	0.608 (0.492; 0.74)	0.597 (0.468; 0.69)	0.749 (0.634; 0.845)	0.869 (0.783; 0.942)	0.755 (0.542; 0.839)
post_ltx vs healthy genus terminal_ileum	0.892 (0.841; 0.962)	0.853 (0.72; 0.927)	0.853 (0.72; 0.927)	0.781 (0.547; 0.871)	0.71 (0.571; 0.796)	0.869 (0.783; 0.942)	0.869 (0.783; 0.942)	0.973 (0.957; 0.987)
pre_ltx vs healthy ASV colon	0.909 (0.83; 0.992)	0.924 (0.865; 0.966)	0.955 (0.914; 0.987)	0.899 (0.766; 0.974)	0.648 (0.459; 0.833)	0.943 (0.878; 0.995)	0.959 (0.912; 0.994)	0.925 (0.836; 0.98)
pre_ltx vs post_ltx ASV colon	0.769 (0.632; 0.853)	0.749 (0.591; 0.87)	0.955 (0.914; 0.987)	0.669 (0.5; 0.829)	0.646 (0.486; 0.759)	0.815 (0.693; 0.936)	0.959 (0.912; 0.994)	0.846 (0.733; 0.915)
post_ltx vs healthy ASV colon	0.877 (0.829; 0.928)	0.937 (0.892; 0.981)	0.955 (0.914; 0.987)	0.898 (0.824; 0.963)	0.721 (0.619; 0.839)	0.961 (0.929; 0.985)	0.959 (0.912; 0.994)	0.976 (0.959; 0.99)
pre_ltx vs healthy genus colon	0.851 (0.758; 0.943)	0.926 (0.832; 0.993)	0.955 (0.914; 0.987)	0.889 (0.756; 0.99)	0.603 (0.476; 0.744)	0.938 (0.857; 0.994)	0.959 (0.912; 0.994)	0.941 (0.9; 0.989)
pre_ltx vs post_ltx genus colon	0.695 (0.516; 0.837)	0.69 (0.541; 0.773)	0.955 (0.914; 0.987)	0.617 (0.5; 0.773)	0.607 (0.488; 0.697)	0.714 (0.535; 0.795)	0.959 (0.912; 0.994)	0.755 (0.542; 0.839)
post_ltx vs healthy genus colon	0.888 (0.834; 0.933)	0.955 (0.914; 0.987)	0.955 (0.914; 0.987)	0.909 (0.879; 0.949)	0.756 (0.61; 0.828)	0.959 (0.912; 0.994)	0.959 (0.912; 0.994)	0.973 (0.957; 0.987)

write.csv(final_df,file=file.path(path,"AUC_all_models.csv"),row.names = FALSE)

ROC - ASV

rocs_list <- supplements_models$roc_cs
rocs_list[["enet_model"]] <- roc_cs

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(1:3)],
                       Q="Q1_czech",
                       model_name=model_name)
}

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))

ROC - genus

plot_list <- list()

for (model_name in names(rocs_list)) {
  plot_list[[model_name]] <- roc_curve_all_custom(rocs_list[[model_name]][c(4:6)],
                       Q="Q1_czech",
                       model_name=model_name)
}

ggarrange(plotlist = plot_list,labels = names(rocs_list),font.label = list(face="plain",size=7))